GPT-5’s Dirty Secret: Why Its Power Consumption Is a Major Environmental Threat

The launch of OpenAI’s GPT-5 has been met with a mix of excitement and deep concern. While its advanced capabilities are impressive, experts are raising the alarm about its potentially massive energy consumption. As the company remains silent on the model’s resource usage, a debate is heating up. They argue that the enhanced abilities of GPT-5, such as its ability to create websites and answer PhD-level questions, come with a steep and unprecedented environmental cost. This lack of transparency from a leading AI developer is raising serious questions about the industry’s commitment to sustainability.
A key piece of evidence comes from a study by the University of Rhode Island’s AI lab. Researchers found that generating a single medium-length response of about 1,000 tokens with GPT-5 can consume an average of 18 watt-hours. This is a dramatic increase from previous models. To put this into perspective, 18 watt-hours is enough energy to power an incandescent light bulb for 18 minutes. Given that a service like ChatGPT handles billions of requests daily, the cumulative consumption could be staggering, potentially reaching the daily electricity demand of millions of homes.
The surge in energy use is directly linked to the model’s size and complexity. Experts believe GPT-5 is significantly larger than its predecessors, with a greater number of parameters. This aligns with a study by French AI company Mistral, which found a strong correlation between a model’s size and its energy consumption. The study concluded that a model ten times bigger will have an impact that is an order of magnitude larger. This principle seems to be holding true for GPT-5, with some experts suggesting its resource use could be “orders of magnitude higher” than even GPT-3.
Compounding the issue is the new model’s architecture. While it does use a “mixture-of-experts” system to improve efficiency, its reasoning capabilities and ability to handle video and images likely counteract these gains. The “reasoning mode,” which involves the model computing for a longer time before generating a response, could make its resource footprint several times greater than text-only operations. This combination of size, complexity, and advanced features paints a clear picture of an AI system with a voracious appetite for power, leading to urgent calls for greater transparency from OpenAI and the broader AI industry.